Molecular pathology is the examination at a molecular level of the DNA, mRNA, and proteins that cause or are otherwise associated with disease. Gene amplification and/or overexpression have been identified as an indicator of patient prognosis in a variety of tumors or for determining those patients that should be provided certain treatments. For example, a certain type of breast cancer is associated with an over-abundance (e.g., over expression) of the human epidermal growth factor 2 (“HER2”) versus the number of chromosome 17s found in the cell. Sadly, this alteration is also an independent prognostic factor predictive of poor clinical outcome and a high risk of recurrence. By detecting the number of HER2 genes versus the number of chromosome 17s in a tissue sample, this particular type of breast cancer can be more readily identified and treatment options can be evaluated.
In-situ hybridization can be used to look for the presence of a genetic abnormality or condition such as amplification of cancer causing genes specifically in cells that, when viewed under a microscope, morphologically appear to be malignant. In situ hybridization (ISH) employs labeled DNA or RNA probe molecules that are anti-sense to a target gene sequence or transcript to detect or localize targeted nucleic acid target genes within a cell or tissue sample. ISH is performed by exposing a cell or tissue sample immobilized on a glass slide to a labeled nucleic acid probe which is capable of specifically hybridizing to a given target gene in the cell or tissue sample. Several target genes can be simultaneously analyzed by exposing a cell or tissue sample to a plurality of nucleic acid probes that have been labeled with a plurality of different nucleic acid tags. By utilizing labels having different emission wavelengths, simultaneous multicolored analysis may be performed in a single step on a single target cell or tissue sample. For example, INFORM HER2 Dual ISH DNA Probe Cocktail Assay from Ventana Medical Systems, Inc., is intended to determine HER2 gene status by enumeration of the ratio of the HER2 gene to Chromosome 17. The HER2 and Chromosome 17 probes are detected using a two color chromogenic ISH in formalin-fixed, paraffin-embedded human breast cancer tissue specimens.
Digital microscopy systems have been introduced wherein tissue samples are prepared in the usual way of being mounted on glass slides, but instead of having the pathologist view the samples using a manually controlled optical microscope, the slides are processed using digital imaging equipment. In recent years, digital pathology has transformed from the use of camera-equipped microscopes to high-throughput digital scanning of whole tissue samples. This development not only enables virtual storing and sharing of biological data, but it also improves the turnaround times for the pathologist and the patient.
The digitization of biological data has enabled the use of computers assisting in the diagnosis. The dramatic increase of computer power over the past decades, together with the development of advanced image analysis algorithms, has allowed the development of computer-assisted approaches capable of analyzing the bio-medical data. Interpreting tissue slides manually is labor intensive, costly and involves the risk of human errors and inconsistency, while using automated image analysis can provide additional automatic, fast and reproducible analyses, assisting the pathologist in making an accurate and timely diagnosis.
Digital images for automated analysis often contain variations that make it difficult to detect and classify differently colored dots representative of, for example, ISH signals. For example, and in the context of dual ISH for HER2 gene expression determination, black dots and red dots may not be as distinguishable as one would like. Other problems may exist including (a) image backgrounds stained with the same or similar color to the signals being detected, (b) optical fringing (chromatic aberrations) of objects within the image, and (c) poor focus of the original image. In addition, it is possible that any dot detection and classification method may (i) incorrectly classify objects whose features are faint (e.g. a faint red dot may get detected but may not be classified properly as a red dot versus a black dot), (ii) fail to detect those dots that are faint; or (iii) in instances where the image contains speckling of small black dots, the speckling may be wrongly picked up and/or classified as black dots.
Thus, there remains a need for an improved method of detecting and classifying dot pixels within cell nuclei that provides a high level of quality and accuracy, while deciphering the multitude of variations aberrations and other “defects” that exist in images.